Missing Data Estimation Using Firefly Algorithm

Collins Achepsah Leke, Tshilidzi Marwala

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Citations (Scopus)


In this chapter, we examine the problem of missing data in high-dimensional datasets by taking into consideration the missing completely at random and missing at random mechanisms, as well as the arbitrary missing pattern. Additionally, this chapter employs a methodology based on deep learning and swarm intelligence algorithms in order to provide reliable estimates for missing data. The deep learning technique is used to extract features from the input data via an unsupervised learning approach by modeling the data distribution based on the input. This deep learning technique is then used as part of the objective function for the swarm intelligence technique in order to estimate the missing data after a supervised fine-tuning phase by minimizing an error function based on the interrelationship and correlation between features in the dataset. The proposed methodology in this chapter, therefore, has longer running times, however, the promising potential outcomes justify the trade-off. Also, basic knowledge of statistics is presumed.

Original languageEnglish
Title of host publicationStudies in Big Data
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
Publication statusPublished - 2019

Publication series

NameStudies in Big Data
ISSN (Print)2197-6503
ISSN (Electronic)2197-6511

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Engineering (miscellaneous)
  • Computer Science Applications
  • Artificial Intelligence


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